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Updated: Jan 27, 2026

Enumeration of Neural Stem Cells Using Clonal Assays

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Published on: October 4, 2016

Transmission Trees on a Known Pathogen Phylogeny: Enumeration and Sampling.

Matthew D Hall¹, Caroline Colijn²

¹Nuffield Department of Medicine, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom.

Molecular Biology and Evolution

|March 16, 2019

Summary

Researchers developed a method to count and sample possible infectious disease transmission trees from pathogen genetic data. This computational approach aids in understanding disease spread dynamics using phylogenetic analysis.

Keywords:

epidemic reconstruction molecular epidemiology pathogen genomics phylogenetics

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Area of Science:

Computational Biology
Epidemiology
Phylogenetics

Background:

Reconstructing infectious disease transmission requires inferring host-to-host spread from pathogen genetic data.
Phylogenetic trees are commonly used, with internal nodes annotated to represent host-pathogen evolution over time.
The mathematical framework for transmission trees derived from phylogenies is not well-established.

Purpose of the Study:

To develop a computational procedure for calculating the number of possible transmission trees consistent with a given phylogenetic tree.
To enable uniform sampling from the space of possible transmission trees.
To extend these methods for various sampling scenarios and time-resolved phylogenies.

Main Methods:

Developed a novel procedure to enumerate all valid transmission trees for a given phylogenetic tree under a complete transmission bottleneck.
Designed an algorithm for uniformly sampling from these possible transmission trees.
Extended the methodology to handle incomplete and multiple sampling per host, and applied it to time-calibrated phylogenies with known infection periods.

Main Results:

Established a method to quantify the number of potential transmission trees derivable from pathogen phylogenies.
Created a uniform sampling strategy for these transmission trees, providing a probabilistic framework for reconstruction.
The STraTUS R package implements the described sampling algorithm for practical application.

Conclusions:

The study provides a foundational mathematical and computational framework for analyzing transmission trees derived from phylogenetic data.
The developed methods and software (STraTUS) facilitate more robust reconstructions of infectious disease transmission pathways.
This work opens avenues for further exploration into the mathematical properties and applications of transmission tree inference.